Method of and apparatus for detecting diseased tissue by sensing two bands of infrared radiation

ABSTRACT

A method of and apparatus for detecting diseased tissue based upon infrared imaging in two different bands of infrared wavelengths is described. The use of two series of infrared images taken in two different bands of infrared wavelengths increases sensitivity to the subtle temperature changes caused by diseased skin and tissue, especially in the case of cancerous tissue. By sensing skin temperature, the homogeneity thereof, the time variations thereof and the correlation between the two series of infrared images, the present invention decreases the rate of false positives and false negatives. The increased discrimination due to two series of infrared images allows for reliable detection of diseased or cancerous tissue even in the presence of skin tone variations such as birthmarks, tattoos and freckles. The present invention finds special application in the field of breast cancer detection where subtle skin temperature variations may readily be sensed using two series of infrared images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of prior, co-pending U.S.patent application Ser. No. 10/625,155, filed 23 Jul. 2003, which isincorporated herein by reference for all purposes.

BACKGROUND

1. Field of the Invention

The present invention relates to a method of and apparatus for detectingdiseased tissue. In particular, the present invention provides anoninvasive method of detecting diseased tissue by sensing skintemperature, the homogeneity thereof, the temporal variation thereof andthe correlation thereof using a sensor detecting two different bands ofinfrared wavelengths.

2. Description of Related Art

In the field of diseased or cancerous tissue detection, many methodsrequire subjecting the patient to doses of X-ray radiation or to painfulbiopsies, especially for breast cancer detection. More recently,researchers discovered that dysfunction of the neuronal control of thevasculature due to cancerous lesions leads to temporal or periodicperfusion changes. By measuring, recording and analyzing these periodicperfusion changes, typically through infrared (IR) imaging, diseased orcancerous tissue can be detected. While these periodic perfusion changesappear to be associated with most types of diseased or cancerous tissue,skin cancer and other cancers near the surface of the skin are mostlikely to be detected using IR imaging. Such a method is described inU.S. Pat. Nos. 5,810,010, 5,961,466 and 5,999,843, all to Michael Anbar,and hereby incorporated by reference.

In particular, breast cancer appears to be very susceptible to detectionthrough IR imaging. Breast cancer detection by this method involvestaking a series of IR images of the breast. This series of IR imageswill show both neuronal control and non-neuronal control of periodicperfusion changes in a cancerous breast. These IR images are thenconverted into thermal images with a temperature associated with eachportion of the thermal image. The thermal images are then analyzed byfinding the average temperature and standard deviation of temperaturefor each subarea within the thermal images. Clusters of subareas havingabnormal average temperatures or standard deviations are indicative ofcancer. It is anticipated that breast cancer may generally be detectedby imaging the appropriate lymph nodes, the so-called “signal nodes,”which tend to have increased biological activity if cancer is present.

The frequency of the periodic perfusion changes can also be used todetect cancer. Neuronal control generally has a lower frequency thannon-neuronal control of periodic perfusion. Therefore, by analyzing thethermal images and determining the periodic perfusion frequency for eachof the subareas, clusters of subareas having higher frequencies areindicative of cancer.

The use of IR images for cancer detection places very stringentrequirements on an IR imager. The small temperature changes associatedwith neuronal and non-neuronal perfusion require an IR imagersensitivity of less than 0.01° C. While IR imagers having this level ofsensitivity have been demonstrated, these IR imagers have notsuccessfully been built in quantity.

In view of the desirability of non-invasive means of cancer detectionthat do not require subjecting the patient to X-ray radiation exposure,there exists a need for a method that places lower requirements upon IRimager sensitivity. A method that requires lower sensitivity will leadto increased manufacturability and lower IR imager cost. Lower cost IRimagers can lead to greater accessibility to cancer screening anddetection.

SUMMARY OF THE INVENTION

A first embodiment of the present invention comprises a method ofdetecting diseased tissue including recording first and second series ofIR images of a predetermined area of tissue. The first and second seriesof IR images are recorded in corresponding first and second bands of IRwavelengths, the two bands of IR wavelengths being different. The firstand second series of IR images are converted into corresponding firstand second series of thermal images. The predetermined area of tissue issubdivided into a plurality of subareas. A first plurality of averagetemperature values is determined for each of the plurality of subareasfrom a corresponding one of the first series of thermal images. A firstaverage temperature is determined using the first plurality of averagetemperature values. A second plurality of average temperature values isdetermined for each of the plurality of subareas from a correspondingone of the second series of thermal images. A second average temperatureis determined using the second plurality of average temperature values.The resulting first and second pluralities of average temperature valuesfor each of the plurality of subareas is then analyzed for possiblediseased tissue. Tissue corresponding to a cluster of at least sixadjacent subareas having a spatial distribution of corresponding firstplurality of average temperature values that is less than about 20% ormore than about 100% of the first average temperature is preliminarilydetermined to be diseased. Tissue corresponding to the clusterpreliminarily determined to be diseased is further analyzed. If thecluster has a spatial distribution of corresponding second plurality ofaverage temperature values that is less than about 20% or more thanabout 100% of the second average temperature, tissue corresponding tothe cluster is confirmed to be diseased.

Another embodiment of the present invention comprises a method ofdetecting diseased tissue including recording first and second series ofIR images of a predetermined area of tissue. The first and second seriesof IR images are recorded in corresponding first and second bands of IRwavelengths, the two bands of IR wavelengths being different. The firstand second series of IR images are converted into corresponding firstand second series of thermal images. The predetermined area of tissue issubdivided into a plurality of subareas. A first plurality of averagetemperature values and a first plurality of temperature standarddeviations are determined for each of the plurality of subareas from acorresponding one of the first series of thermal images. A secondplurality of average temperature values and a second plurality oftemperature standard deviations are determined for each of the pluralityof subareas from a corresponding one of the second series of thermalimages. For each of the plurality of subareas, each corresponding one ofthe first plurality of average temperature values is divided by acorresponding one of the first plurality of temperature standarddeviations to determine a corresponding one of a first plurality ofhomogeneity of skin temperature (HST) values for the plurality ofsubareas. For each of the plurality of subareas, each corresponding oneof the second plurality of average temperature values is divided by acorresponding one of the second plurality of temperature standarddeviations to determine a corresponding one of a second plurality of HSTvalues for the plurality of subareas. A first average HST value isdetermined using the first plurality of HST values while a secondaverage HST value is determined using the second plurality of HSTvalues. The resulting first and second pluralities of HST values foreach of the plurality of subareas are then analyzed for possiblediseased tissue. Tissue corresponding to a cluster of at least sixadjacent subareas having a corresponding spatial distribution of firstplurality of HST values that is less than about 20% or more than about100% of the first average HST value is preliminarily determined to bediseased. Tissue corresponding to the cluster preliminarily determinedto be diseased is further analyzed. If the cluster has a correspondingspatial distribution of second plurality of HST values that is less thanabout 20% or more than about 100% of the second average HST value,tissue corresponding to the cluster is confirmed to be diseased.

In yet another embodiment, the present invention comprises a method ofdetecting diseased tissue including recording first and second series ofinfrared images of a predetermined area of tissue. The first and secondseries of infrared images are recorded in respective first and secondbands of infrared wavelengths, with the second band of infraredwavelengths different from the first band of infrared wavelengths. Thefirst and second series of infrared images are converted intocorresponding first and second series of thermal images. Thepredetermined area of tissue is subdivided into a plurality of subareas.A first plurality of average temperature values is determined for eachof the plurality of subareas, with each of the first plurality ofaverage temperature values for each of the plurality of subareas beingdetermined from one of the first series of thermal images. A secondplurality of average temperature values is determined for each of theplurality of subareas, with each of the second plurality of averagetemperature values for each of the plurality of subareas beingdetermined from one of the second series of thermal images. First andsecond radiance measurements are taken at respective first and secondbands of infrared wavelengths of known healthy tissue. The first andsecond radiance measurements of known healthy tissue are correlated. Thefirst and second plurality of average temperature values for each of theplurality of subareas are correlated. The correlated first and secondplurality of average temperature values for each of the plurality ofsubareas is then analyzed. When a spatial distribution of slopes of thecorrelated first and second plurality of average temperature valuescorresponding to a cluster comprising at least six adjacent subareas isdifferent from a slope of the correlation of known healthy tissue,tissue corresponding to the cluster is determined to be diseased.

In still another embodiment, the present invention comprises a method ofdetecting diseased tissue including recording first and second series ofIR images of a predetermined area of tissue. The first and second seriesof IR images are recorded in corresponding first and second bands of IRwavelengths, the two bands of IR wavelengths being different. The firstand second series of IR images are converted into corresponding firstand second series of thermal images. The predetermined area of tissue issubdivided into a plurality of subareas. A first plurality of averagetemperature values and a first plurality of temperature standarddeviations are determined for each of the plurality of subareas from acorresponding one of the first series of thermal images. A secondplurality of average temperature values and a second plurality oftemperature standard deviations are determined for each of the pluralityof subareas from a corresponding one of the second series of thermalimages. A first average temperature standard deviation is determinedusing the first plurality of temperature standard deviations. A secondaverage temperature standard deviation is determined using the secondplurality of temperature standard deviations. The resulting first andsecond pluralities of temperature standard deviations for each of theplurality of subareas are then analyzed for possible diseased tissue.Tissue corresponding to a cluster of at least six adjacent subareashaving a spatial distribution of corresponding temperature standarddeviations that is less than about 20% or more than about 100% of thefirst average temperature standard deviation is preliminarily determinedto be diseased. Tissue corresponding to the cluster preliminarilydetermined to be diseased is further analyzed. If the cluster has acorresponding spatial distribution of second plurality of temperaturestandard deviations that is less than about 20% or more than about 100%of the second average temperature standard deviation, tissuecorresponding to the cluster is confirmed to be diseased.

In further embodiments, alternative data analysis is possible. Thisalternative data analysis may include finding contributing frequenciesfor each subarea and determining that tissue corresponding to a clusterhaving a spatial distribution of less than a lower threshold frequencyor more than an upper threshold frequency is diseased. The data mayundergo fast Fourier analysis for this frequency determination. The datain the two series of thermal images can be correlated with diseasedtissue having a different correlation intercept than healthy tissue. Thecontrast in the two series of IR images can be enhanced by subjectingthe predetermined area of tissue to thermal stress, such as by directinga cooling flow of air across the area of tissue.

DESCRIPTION OF THE DRAWINGS

The present invention is described in reference to the followingDetailed Description and the drawings in which:

FIG. 1 is diagram of the blood perfusion process that can be detected byembodiments of the present invention,

FIG. 2 is an average temperature histogram generated by a firstembodiment of the present invention,

FIG. 3 is a contributing frequency histogram generated by second andthird embodiments of the present invention,

FIG. 4 is a correlation plot generated by fourth and fifth embodimentsof the present invention,

FIG. 5 is an HST value histogram generated by sixth and seventhembodiments of the present invention,

FIG. 6 is a temperature standard deviation histogram generated by aneighth embodiment of the present invention

FIG. 7 is a block diagram of an apparatus for implementing the firstthrough third embodiments of the present invention,

FIG. 8 is a block diagram of an apparatus for implementing the fourthand fifth embodiments of the present invention,

FIG. 9 is a block diagram of an apparatus for implementing the sixth andseventh embodiments of the present invention, and

FIG. 10 is a block diagram of an apparatus for implementing the eighthembodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Various embodiments of the present invention are described in detailwith reference to the drawings. While the following description willgenerally discuss each embodiment separately, two or more embodimentsmay be combined to increase the accuracy of diseased or cancerous tissuedetection. Further, while the present description will generally useonly two bands of IR wavelengths, the use of three or more bands of IRwavelengths will further increase system sensitivity to diseased tissue.

FIG. 1 illustrates an area of tissue and skin 100, of which a portion isdiseased, such as by a cancerous lesion. This area of tissue and skin100 is imaged by a diagnostic system 110 employing the methodology ofthe present invention. The diagnostic system 110 comprises a dual-bandIR imager 112 and a computer 114.

In the healthy portion of the area of tissue and skin 100, the bodyregulates its temperature using neuronal modulation of blood perfusion120. The neuronal modulation of blood perfusion 120 includesvasodilation to cool the body and vasoconstriction to warm the body inthe body's effort to maintain a desired temperature 122. This results innormal temperature oscillations 124 about the desired temperature 122.The body uses the skin as a radiator to remove excess heat causing theskin temperature 126 to oscillate. The skin temperature 126 oscillatesover a band of neuronal thermoregulatory frequencies (TRFs) 128. Theskin therefore radiates an IR flux 130 as excess heat is given off bythe skin in the body's effort to maintain the desired temperature 122.While this process is generally discussed in terms that the tissueunderlying the skin is cancerous, this method lends itself to thedetection of skin cancer as well. For that reason, while the term tissueand skin may be used separately, skin will also be considered tissue forthe purposes of this description.

The diagnostic system 110 takes a series of infrared images of thetissue and skin 100 using the dual-band IR imager 112 and processes theresultant images using the computer 114. The actual images will becomposed of many individual pixels, each corresponding to a differentportion of the imaged tissue and skin 100. The dual-band IR imager 112may be based upon a 256 pixel by 256 pixel or 480 pixel by 640 pixeldual-band IR photodetector array. To increase sensitivity, the dual-bandIR imager 112 images the tissue and skin 100 in two different bands ofIR wavelengths resulting in two different series of IR images. By usingthe two different series of IR images, the occurrence of false positivesand false negatives may be reduced. The second series of IR images inthe second band of IR wavelengths may serve as a check on the firstseries of IR images in the first band of IR wavelengths, therebyincreasing overall diagnostic system 110 sensitivity depending upon thedata analysis method. The use of N independent bands of IR wavelengthsgenerally leads to a √N increase in sensitivity. With the two bands usedthroughout this description, this increase in sensitivity leads from asingle band IR diagnostic system having a sensitivity of 30 m° C. to adual-band IR diagnostic system 110 having a sensitivity of 21 m° C.Alternatively, if 30 m° C. is the desired diagnostic system 110sensitivity, then the dual-band IR imager 112 can incorporate twosingle-band IR photodetector arrays each having a sensitivity of 42 m°C., thereby improving manufacturability.

The increased sensitivity of the dual-band IR imager 112 over asingle-band IR imager decreases the occurrence of false positive andfalse negatives due to tissue and skin variations. Different portions ofthe skin may radiate different levels of IR flux, even though both theskin and the underlying tissue are healthy. As an example, a birthmarkwill likely radiate heat differently than normal skin. Similarly, atattoo may create a false positive or false negative, as it too willradiate heat differently than normal skin. For a very sensitivesingle-band IR imager, a large freckle may lead to a false positive orfalse negative. However, by using two series of IR images, each taken indifferent bands of IR wavelengths, false positives and false negativesdue to variations in skin color will be minimized. Variations in theunderlying tissue can also affect detection of diseased tissue. While abreast may have relatively uniform tissue, an arm will include areas ofsignificant muscle tissue adjacent to bony regions such as the elbow andwrist, resulting in IR image variations.

The nitric oxide (NO) modulation of blood perfusion 140 will bedescribed next. A diseased portion of the tissue and skin 100, due to acancer 142 in this discussion, provokes an immune response 144 withinthe tissue and skin 100. This immune response 144 results in increasedmacrophage activity 146, which produces NO 148. Some cancers, such asbreast cancer, are known to elevate the local level of ferritin 150within the diseased tissue. Elevated levels of ferritin 150 furtherincreases the amount of NO 148 produced within the diseased tissue.Nitric oxide causes vasodilation 152 of the capillary bed leading toenhanced blood perfusion 156 within the diseased tissue. A side effectof the presence of NO is that neuronal control (vasodilation andvasoconstriction) of the capillary bed is impaired 154. The net resultis that temperature in the diseased tissue will be controlled more byNO-based blood perfusion rather than by neuronal processes. That is, NOcontrolled temperature oscillations 158 will dominate over theattenuated neuronal temperature oscillations 160.

A second side effect of NO controlled blood perfusion is an increase inspatial homogeneity of skin temperature 162. That is, there will be lesstemperature variation in the skin surface temperature due to theNO-induced vasodilation of the capillary bed. NO controlled bloodperfusion will occur at non-neuronal TRFs 164, as will be discussed indetail below. As with healthy tissue, the temperature of the skinoverlying diseased tissue will create an IR flux 166 that can then beimaged by the dual-band IR imager 112.

The first embodiment of the present invention is based upon the averagetemperature of the imaged tissue. The first embodiment converts thefirst and second series of IR images into thermal images, i.e., convertseach pixel from the IR image to a corresponding temperature. Eachindividual thermal image therefore is a two-dimensional array oftemperatures and each of the first and second series of thermal imagesis a series of two-dimensional arrays of temperatures. At the preferredimaging rate of 30 to 60 images per second and a 10 to 60 second seriesof images, the first and second thermal images can readily include over1000 individual thermal images. The first embodiment next subdivides thetissue area imaged into a number of subareas. These subareas correspondto two pixel by two pixel portions of the thermal images or larger. Apreferred upper limit on the subarea size is an eight pixel by eightpixel subarea as larger areas will tend to average out any localvariations that might indicate the presence of diseased tissue.

The first embodiment then finds the average temperature value for eachof these subareas. This is done for each individual thermal image inboth the first and the second series of thermal images resulting infirst and second pluralities of average temperature values. These firstand second pluralities of average temperature values are then analyzedin view of FIG. 2. FIG. 2 illustrates a histogram showing all of theaverage temperature values for the first plurality of averagetemperature values 200. Curve 202 is the composite curve showing theaverage temperature values for skin overlying both healthy and diseasedtissue. Curve 204 corresponds to the average temperature values for theskin overlying a healthy region of tissue. Curve 204 thereforecorresponds to skin whose underlying tissue is thermally regulated byneuronal control of blood perfusion. The peak temperature value for thishealthy tissue is denoted T_(H). In regions of skin overlying diseasedor cancerous tissue, the average temperature value curve 206 is formed.Due to the generally vasodilated state of the capillary bed in diseasedtissue, the average temperature value for these regions is greater. Thehigher peak average temperature value for these diseased regions isdenoted by T_(D).

A preliminary determination that a cancerous lesion may be presentrequires that a cluster of six adjacent subareas each have abnormalaverage temperature values. A first average temperature value for thefirst series of thermal images is calculated. This first averagetemperature value is preferably found by proportionately weighting eachof the subareas based upon their size. In particular, when a spatialdistribution of the first average temperature values within the clusterof six adjacent subareas is less than about 20% or more than about 100%of the first average temperature value, tissue corresponding to thecluster of six adjacent subareas is preliminarily determined to bediseased. This preliminary determination is confirmed if the same seriesof calculations and comparisons on the second series of thermal imagesyields the same cluster of six adjacent subareas.

As each of the first and second series of IR images is preferably takenperiodically, TRFs can be determined. The second embodiment of thepresent invention makes use of these TRFs. FIG. 3 illustrates a TRFhistogram for both healthy and diseased tissue 300. Curve 302 is acomposite for both the healthy and diseased tissue while curve 304corresponds to healthy tissue and curve 306 corresponds to diseasedtissue. Curve 304 for healthy tissue reflects neuronal control bloodperfusion and generally has a frequency of between 10 and 700milliHertz. In contrast, curve 306 for diseased tissue reflects NO-basedcontrol of blood perfusion and has a higher frequency, generally in therange of 0.8 to 2.0 Hz.

The second embodiment makes use of the differences in TRFs by findingthe contributing frequency for each subarea in the first series ofthermal images. This contributing frequency may be determined byanalyzing the average temperature value for a subarea based on the knownperiodic nature of the first series of thermal images. The preferredmethod to determine the contributing frequency is to subject the averagetemperature values to a fast Fourier transform that rapidly finds thefrequency components or ranges of frequencies for a time varying signal.As shown in FIG. 3, while more healthy tissue subareas had a TRF ofF_(H), there is some variation about this frequency. However, very fewhealthy tissue subareas had a TRF as high as F_(D), the strongest of thediseased tissue TRFs. Once the contributing frequency for each subareausing the first series of thermal images is determined, first lower andupper threshold frequencies are found, preferably by weighting eachsubarea based upon their size. As before, a cluster of six adjacentabnormal subareas leads to a preliminary diseased tissue diagnosis. Inparticular, when a spatial distribution of the contributing frequency ofthe cluster is less than the first lower threshold frequency or morethan the first upper threshold frequency, tissue corresponding to thecluster is preliminarily diagnosed as being diseased. This preliminarydiagnosis is confirmed if the same series of determinations andcomparisons on the second series of thermal images yields the samecluster of six adjacent subareas.

The third embodiment is similar to the second embodiment in that it usesthe contributing frequency of each subarea. In particular, the thirdembodiment uses the amplitude of the contributing frequencies. As shownin FIG. 3, the diseased tissue curve 306 has only a small frequencyamplitude at F_(H), thus providing another means for cancerdiscrimination. The third embodiment therefore searches for a cluster inwhich a spatial distribution of the amplitude of the contributingfrequency is less than a first lower threshold amplitude or more than afirst upper threshold amplitude. The first lower and upper thresholdamplitudes are determined using the first series of thermal images andis preferably weighted by subarea size. As with the previousembodiments, the use of the second series of thermal images is used toconfirm a preliminary diseased diagnosis from the first series ofthermal images.

In contrast to the first three embodiments that use the two series ofthermal images sequentially, the fourth embodiment uses the two seriesof thermal images in parallel. FIG. 4 illustrates a series ofcorrelation curves 400 for two different bands of IR wavelengths, thetwo bands centered around λ₁ and λ₂. The fourth embodiment includestaking a baseline radiance measurement of known healthy skin and tissuein the two different bands of IR wavelengths, thereby generating ahealthy skin and tissue correlation curve 402. This healthy correlationcurve 402 can be mathematically defined most simply in terms of a slopeand an intercept, that is λ₂=m_(H)λ₁+b_(H). It should be noted thatdepending upon the wavelengths within the two bands of IR wavelengths,the properties of the skin and underlying tissue, etc., additional termsmight be required to more accurately describe the correlation. In thesimple slope and intercept form, the precise values for m_(H) and b_(H)will likely be a function of the skin and the underlying tissue. Forexample, the m_(H) and b_(H) values for a breast cancer screening willlikely be different from the m_(H) and b_(H) values for a bony structuresuch as the wrist or ankle. Once the appropriate healthy correlationcurve 402 is determined, the subareas within the first and second seriesof thermal images will also be correlated. This correlation may producesubareas having diseased correlation curve 404 or 406. Diseasedcorrelation curve 404 may be described as λ₂=m_(D1)λ₁+b_(D1), whilediseased correlation curve 406 may be described as λ₂=m_(D2)λ₁+b_(D2).The fourth embodiment then compares the slope m_(D1) or mD_(D2) withm_(H). If a spatial distribution of the m_(D1) or m_(D2) values for acluster are different than m_(H), then the tissue corresponding to thecluster is determined to be diseased. How different the slope valueswill be will depend upon the types of underlying tissue as noted above,as well as the specific wavelengths λ₁ and λ₂ chosen.

The radiance measurements of healthy skin taken for the fourthembodiment may be made as a function of integration time for thedual-band IR imager 112, the temperature of the skin and tissue beingimaged, or a combination thereof. The temperature of the skin and tissuecan be varied by directing either a warming or a cooling stream of airon the skin and tissue resulting in thermal stress to the skin andtissue. Alternatively, this thermal stress may be induced by directing aflow of water vapor to the skin and tissue. While this thermal stressfinds particular application with the fourth (and fifth) embodiments, itcan readily be used in conjunction with the other embodiments as well.

Due to the oscillatory nature of thermal regulation, the sensitivity ofthe fourth (and fifth) embodiments can be increased. By finding thecontributing frequency for each of the subareas, the correlation betweenthe two series of thermal images can be made at neuronal frequencies orat NO modulation frequencies. It is anticipated that correlations madeat NO modulation frequencies will be especially sensitive fordiscriminating healthy versus diseased skin and tissue regions.

While the fourth embodiment uses the slope of the correlation betweenthe two series of thermal images, the fifth embodiment uses theintercept of the correlation between the two series of thermal images.To this end, the fifth embodiment compares b_(D1) or b_(D2) with b_(H).When the spatial distribution of b_(D1) or b_(D2) for a cluster aredifferent from b_(H), tissue corresponding to the cluster is diagnosedas being diseased. As before, this difference is a function of theunderlying tissue and the specific wavelengths chosen.

The sixth embodiment of the present invention is based upon detectabledifferences in the HST between healthy and diseased skin and tissue. TheHST for a subarea is found by determining both the average temperaturevalue and the temperature standard deviation and then dividing theaverage temperature value by the temperature standard deviation. The HSTis found for each subarea for each of the first series of thermalimages. FIG. 5 shows the resultant histogram 500 of HST values from thefirst series of thermal images for the skin overlying both healthy anddiseased tissue. Curve 502 is the overall HST curve while curve 504corresponds to healthy skin and tissue while curve 506 corresponds todiseased skin and tissue. The temperature standard deviation found indiseased tissue is lower than that of healthy tissue due to the overallvasodilated state of the capillary bed. This lower standard deviationresults in higher HST values for diseased skin and tissue regions,centered about HST_(D) as shown in FIG. 5. In contrast, healthy skin andtissue temperature is controlled by neuronal processes that include bothvasodilation and vasoconstriction. This results in wider variations inskin temperature, larger temperature standard deviations and thereforesmaller HST values. FIG. 5 shows the healthy skin and tissue regions tohave HST values centered about HST_(H). An overall first average HST forthe first series of thermal images is also computed. A preliminarydiseased tissue diagnosis is made when spatial distribution of a clusterof six adjacent subareas have HST values of less than about 20% or morethan about 100% of the first average HST. This preliminary diagnosis isconfirmed if the same series of calculations and comparisons on thesecond series of thermal images yields the same cluster of six adjacentsubareas.

The seventh embodiment makes use of the differences in TRFs of the HSTvalues by finding the contributing frequency for each subarea in thefirst plurality of HST values. The seventh embodiment will generate afrequency histogram similar to that of FIG. 3 in that healthy tissuesubareas will have a TRF of HST values with some variation about ahealthy tissue center frequency. Likewise, diseased tissue subareas willhave TRF of HST values with some variation about a higher diseasedtissue center frequency. Once the contributing TRF of HST values foreach subarea using the first series of thermal images is determined, afirst average contributing frequency is found. A cluster of six adjacentabnormal subareas leads to a preliminary diseased tissue diagnosis. Inparticular, when a spatial distribution of the magnitude of thecontributing TRF of HST values of the cluster is less than about 20% ormore than about 100% of the first average contributing frequency, tissuecorresponding to the cluster is preliminarily diagnosed as beingdiseased. This preliminary diagnosis is confirmed if the same series ofdeterminations and comparisons on the second series of thermal imagesyields the same cluster of six adjacent subareas.

FIG. 6 illustrates a temperature standard deviation histogram 600employed by the eighth embodiment of the present invention. The eighthembodiment requires determining the temperature standard deviation foreach of the subareas for each one of the first series of thermal images.Curve 602 corresponds to the resultant overall histogram for thetemperature standard deviations and is a combination of a curve 604representing the temperature standard deviations for healthy skin andtissue and curve 606 representing the temperature standard deviationsfor diseased skin and tissue. The standard deviation for diseased skinand tissue will be lower as noted above due to the generally vasodilatedstate of the capillary bed leading to more constant temperaturesrelative to skin and tissue under neuronal controlled blood perfusion. Apreliminary diagnosis of diseased skin and tissue corresponding to acluster of six adjacent subareas requires the cluster to have a spatialdistribution of temperature standard deviation of less than about 20% ormore than about 100% of a first average temperature standard deviationbased upon the first series of thermal images. The preliminary diagnosisbased upon temperature standard deviation is confirmed if the sameseries of determinations and comparisons on the second series of thermalimages yields the same cluster of six adjacent subareas.

Each of the embodiments will now be described in reference to FIGS. 7through 10. The first through third embodiments are illustrated by theblock diagram shown in FIG. 7. In each of the first through thirdembodiments, two series of IR images of the tissue are recorded in twocorresponding different bands of IR wavelengths by the dual-band IRimager 112. The two series of IR images are then converted by aconverter 704 into two series of thermal images. An averager 706 thendetermines a series of average temperatures for each of the subareasusing both series of thermal images. The averager 706 also determines anoverall average temperature using both series of thermal images. All ofthis average temperature information is then analyzed by an analyzer 708in the first embodiment. In the second embodiment, the two series ofthermal images undergo frequency analysis, i.e., the contributingfrequencies for the subareas are determined, by a frequency analyzer710. The contributing frequencies are then analyzed by the analyzer 708to determine if any clusters indicate the presence of diseased tissuebased upon contributing frequencies. Like the second embodiment, thethird embodiment uses the frequency analyzer 710. The third embodimentrequires the analyzer to analyze the amplitude of the contributingfrequencies and any clusters having unusual frequency amplitudes may bediagnosed as corresponding to diseased tissue.

The fourth and fifth embodiments are illustrated in the block diagram ofFIG. 8. As with the first three embodiments, two series of IR images ofthe tissue are recorded in two corresponding different bands of IRwavelengths by the dual-band IR imager 112. The two series of IR imagesare then converted by the converter 704 into two series of thermalimages. The averager 106 then determines a series of averagetemperatures for each of the subareas using both series of thermalimages. The dual-band IR imager 112 also records radiance images in bothbands of IR wavelengths, which are subsequently converted into thermalimages. Both sets of average temperature data and the radiance imagedata are correlated by a correlator 722. An analyzer 724 then analyzesthe correlation data produced by the correlator 722. In the fourthembodiment, the analyzer 724 analyzes the slope of the correlation datawhile in the fifth embodiment the analyzer 724 analyzes the intercept ofthe correlation data. FIG. 8 also illustrates an element 726 forsubjecting tissue to a thermal stress. As noted above, the element 726can create this thermal stress by directing a stream of warm or cool airover the tissue or by directing a mist at the tissue. While the element726 is illustrated only in FIG. 8 corresponding to the apparatus forimplementing the fourth and fifth embodiments, it can readily beincluded apparatuses for implementing the first through third and sixththrough eighth embodiments.

An apparatus for implementing the sixth and seventh embodiments isillustrated in block fashion in FIG. 9. As with the first fiveembodiments, two series of IR images of the tissue are recorded in twocorresponding different bands of IR wavelengths by the dual-band IRimager 112. The two series of IR images are then converted by theconverter 704 into two series of thermal images. In the sixthembodiment, the two series of thermal images are then processed by theprocessor 744. The processor 744 determines average temperatures andstandard deviations for each of the subareas using both series ofthermal images. The processor 744 then determines HST values for each ofthe subareas for both series of thermal images. Lastly, the processor744 determines the average HST value for both series of thermal images.An analyzer 746 then analyzes this HST data to determine if any clusterscorrespond to diseased tissue. In the seventh embodiment, the two seriesof thermal images undergo frequency analysis by the frequency analyzer710. The resultant frequency analyzed data is then analyzed by theanalyzer 746 to determine of diseased tissue is present.

FIG. 10 illustrates the various blocks required for implementing theeighth embodiment of the present invention. Two series of IR images ofthe tissue are recorded in two corresponding different bands of IRwavelengths by the dual-band IR imager 112. The two series of IR imagesare then converted by a converter 704 into two series of thermal images.These two series of thermal images then undergo a series of processes bythe processor 744 described above. The various averaged data is thenanalyzed by an analyzer 764. In the eighth embodiment, the analyzer 764determines if any clusters have abnormal standard deviations that wouldindicate the presence of diseased tissue.

The diagnostic system 110, and in particular, the dual-band IR imager112 will now be described in greater detail. The first and second bandsof IR wavelengths detected by the dual-band IR imager 112 are preferablywithin the long wavelength IR (LWIR), which corresponds to radiationhaving a wavelength of eight to twelve microns. For example, the firstband of IR wavelengths might cover the wavelength range of eight to ninemicrons while the second band of IR wavelengths might cover from ten toeleven microns. The LWIR is preferred as the human body IR emissionspeak within this range of wavelengths. The first and second bands of IRwavelengths could alternatively be in the middle wavelength IR (MWIR)corresponding to radiation having a wavelength of three to five microns.As a further alternative, the two bands of IR wavelengths could includeone in the LWIR and one in the MWIR.

The dual-band IR imager 112 may be formed in one of several ways. Thedual-band IR imager 112 could include two single-band IR photodetectorarrays, each sensitive to different bands of IR wavelengths.Alternatively, the two single-band IR photodetector arrays could beidentical with the different bands of IR wavelength response due tofilters. Using two single band IR photodetectors will require the use ofa beam splitter to cause spatially registered images to be focused oneach of the single-band IR photodetector arrays. While the use of twosingle-band IR photodetector arrays will probably decrease the cost ofeach single-band IR photodetector array, the overall system cost willlikely increase. Such a two photodetector array-based dual-band IRimager 112 will require the aforementioned beamsplitter, and probablytwo separate coolers as each single-band IR photodetector array willrequire cooling. Such a two photodetector array-based dual-band IRimager will also require very tight tolerances to ensure that the imageis truly spatially registered on both photodetector arrays, therebyreducing manufacturability.

A single dual-band IR photodetector array appears more feasible andmanufacturable. Several dual-band photodetector technologies have beendemonstrated including those using HgCdTe and GaAs-based multiplequantum well (MQW) semiconductor materials. Dual-band photodetectorsusing HgCdTe semiconductor materials have high quantum efficiencies, butplace strict requirements on the HgCdTe manufacturing process. Whiledual-band HgCdTe photodetectors operating in the MWIR and LWIR haveshown excellent performance, the use of HgCdTe semiconductor materialfor the preferred LWIR-LWIR configuration places extremely strictrequirements on the starting HgCdTe semiconductor material. For thesereasons, it appears unlikely that a commercial HgCdTe dual-band IRcamera is feasible using current manufacturing technology.

GaAs-based MQW semiconductor material appears to be a moremanufacturable technology and is thus preferable for the presentinvention. The GaAs-based starting material is commercially availablefrom several sources and the fabrication processes are in use in anumber of facilities. GaAs-based MQW semiconductor material may befabricated into quantum well IR photodetectors (QWIPs) and enhancedQWIPs (EQWIPs). Dual-band QWIPs and EQWIPs have been demonstrated todate with the EQWIP offering better sensitivity due to its resonantoptical cavity and reduced noise. Various embodiments of the EQWIP aredescribed and claimed in U.S. Pat. Nos. 5,539,206, 6,133,571, 6,157,042,and 6,355,939 and are hereby incorporated by reference. Additionalpreferred embodiments of the EQWIP are described in copendingapplication numbers 21201 and 21301.

The present invention, by imaging a human being, encounters problemsshould the patient move during the image taking portion of the process.To minimize this effect, the images for the two different series of IRimages are preferably taken in an alternating fashion. That is, first anIR image is taken from the first band of IR wavelengths and then an IRimage is taken from the second band of IR wavelengths. By alternatingthe IR wavelength bands, the correlation between the first image in bothseries of IR images increases when compared to taking all of the firstseries of IR images over the course of 10 to 60 seconds and then takingall of the second series of IR images. To further minimize problems dueto patient motion, the imaging rate should be relatively high,preferably in the range of 30 to 60 Hz or greater. An added benefit ofthe increased imaging rate is that any of the embodiments usingfrequency-based analysis will have increased frequency resolution.

The computer 114 within the diagnostic system 110 will be required tostore significant quantities of data and undertake substantial numericalprocessing. The computer 114 will need to store several thousands ofindividual IR images and thermal images for each patient. As each ofthese could include 640 pixels by 480 pixels-worth of data, a rathersizeable hard disk drive and large amount of RAM will be beneficial. Dueto the substantial amount of numerical processing that will beundertaken, a separate numerical processing board may be advantageous.

Although the present invention has been fully described by way ofexamples with reference to the accompanying drawings, it is to be notedthat various changes and modifications will be apparent to those skilledin the art. Therefore, such changes and modifications should beconstrued as being within the scope of the invention.

1. A method of detecting diseased tissue comprising: recording first andsecond series of infrared images of a predetermined area of tissue, thefirst and second series of infrared images recorded in respective firstand second bands of infrared wavelengths, the second band of infraredwavelengths different from the first band of infrared wavelengths;converting the first and second series of infrared images intocorresponding first and second series of thermal images having aplurality of subareas; determining a first plurality of averagetemperature values and a first plurality of temperature standarddeviations for each of the plurality of subareas, each of the firstplurality of average temperature values and each of the first pluralityof temperature standard deviations corresponding to each of theplurality of subareas determined from a corresponding one of the firstseries of thermal images; determining a second plurality of averagetemperature values and a second plurality of temperature standarddeviations for each of the plurality of subareas, each of the secondplurality of average temperature values and each of the second pluralityof temperature standard deviations corresponding to each of theplurality of subareas determined from a corresponding one of the secondseries of thermal images; for each of the plurality of subareas,dividing each corresponding one of the first plurality of averagetemperature values by a corresponding one of the first plurality oftemperature standard deviations thereby determining a corresponding oneof a first plurality of homogeneity of skin temperature values for theplurality of subareas; determining a first average homogeneity of skintemperature value from the first plurality of homogeneity of skintemperature values; for each of the plurality of subareas, dividing eachcorresponding one of the second plurality of average temperature valuesby a corresponding one of the second plurality of temperature standarddeviations thereby determining a corresponding one of a second pluralityof homogeneity of skin temperature values for the plurality of subareas;determining a second average homogeneity of skin temperature value fromthe second plurality of homogeneity of skin temperature values; andanalyzing the first and second pluralities of homogeneity of skintemperature values for each of the plurality of subareas, wherein when aspatial distribution of first plurality of homogeneity of skintemperature values corresponding to a cluster comprising at least sixadjacent subareas is less than about 20% or more than about 100% of thefirst average homogeneity of skin temperature value, tissuecorresponding to the cluster is preliminarily determined to be diseased,and wherein when tissue corresponding to the cluster is preliminarilydetermined to be diseased and when a spatial distribution of secondplurality of homogeneity of skin temperature values corresponding to thecluster is less than about 20% or more than about 100% of the secondaverage homogeneity of skin temperature value, tissue corresponding tothe cluster is confirmed to be diseased.
 2. A method of detectingdiseased tissue in accordance with claim 1, wherein analyzing the firstand second pluralities of homogeneity of skin temperature values foreach of the plurality of subareas further includes: determining a firstcontributing frequency of the first plurality of homogeneity of skintemperature values for each of the plurality of subareas using the firstseries of thermal images; determining first lower and upper thresholdfrequencies using the first contributing frequency of each of thesubareas; determining a second contributing frequency of the secondplurality of homogeneity of skin temperature values for each of theplurality of subareas using the second series of thermal images; anddetermining second lower and upper threshold frequencies using thesecond contributing frequency of each of the subareas, wherein when afirst spatial distribution of contributing frequencies of the cluster isless than the first lower threshold frequency or more than the firstupper threshold frequency, tissue corresponding to the cluster ispreliminarily determined to be diseased, and wherein when tissuecorresponding to the cluster is preliminarily determined to be diseasedand when a spatial distribution of second contributing frequencies ofthe cluster is less than the second lower threshold frequency or morethan the second upper threshold frequency, tissue corresponding to thecluster is confirmed to be diseased.
 3. A method of detecting diseasedtissue in accordance with claim 1, wherein analyzing the first andsecond pluralities of homogeneity of skin temperature values for each ofthe plurality of subareas further includes: determining a firstcontributing frequency of the first plurality of homogeneity of skintemperature values for each of the plurality of subareas using the firstseries of thermal images; determining a first average amplitude of thefirst contributing frequency for the plurality of subareas; determininga second contributing frequency of the second plurality of homogeneityof skin temperature values for each of the plurality of subareas usingthe second series of thermal images; and determining a second averageamplitude of the second contributing frequency for the plurality ofsubareas, wherein when a spatial distribution of first amplitudes offirst contributing frequencies of the cluster is less than about 20% ormore than about 100% of the first average amplitude, tissuecorresponding to the cluster is preliminarily determined to be diseased,and wherein when tissue corresponding to the cluster is preliminarilydetermined to be diseased and when a spatial distribution of secondamplitudes of second contributing frequencies of the cluster is lessthan about 20% or more than about 100% of the second average amplitude,tissue corresponding to the cluster is confirmed to be diseased.
 4. Amethod of detecting diseased tissue in accordance with claim 1, whereinanalyzing the first and second pluralities of average temperature valuesfor each of the plurality of subareas further includes analyzing thefirst and second pluralities of average temperature values for each ofthe plurality of subareas using a fast Fourier transform analysis.
 5. Amethod of detecting diseased tissue in accordance with claim 1, whereinthe first and second series of infrared images of the predetermined areaof tissue are recorded when the predetermined area of tissue issubjected to a thermal stress.
 6. A method of detecting diseased tissuein accordance with claim 5, wherein the thermal stress is induced by aflow of air.
 7. A method of detecting diseased tissue in accordance withclaim 5, wherein the thermal stress is induced by a water mist.
 8. Anapparatus for detecting diseased tissue comprising: an imager forrecording first and second series of infrared images of a predeterminedarea of tissue, the first and second series of infrared images recordedin respective first and second bands of infrared wavelengths, the secondband of infrared wavelengths different from the first band of infraredwavelengths; a converter for converting the first and second series ofinfrared images into corresponding first and second series of thermalimages having a plurality of subareas; a processor for determining afirst plurality of average temperature values and a first plurality oftemperature standard deviations for each of the plurality of subareas,each of the first plurality of average temperature values and each ofthe first plurality of temperature standard deviations corresponding toeach of the plurality of subareas determined from a corresponding one ofthe first series of thermal images, the processor for determining asecond plurality of average temperature values and a second plurality oftemperature standard deviations for each of the plurality of subareas,each of the second plurality of average temperature values and each ofthe second plurality of temperature standard deviations corresponding toeach of the plurality of subareas determined from a corresponding one ofthe second series of thermal images, for each of the plurality ofsubareas, the processor for dividing each corresponding one of the firstplurality of average temperature values by a corresponding one of thefirst plurality of temperature standard deviations thereby determining acorresponding one of a first plurality of homogeneity of skintemperature values for the plurality of subareas, the processor fordetermining a first average homogeneity of skin temperature value fromthe first plurality of homogeneity of skin temperature values, for eachof the plurality of subareas, the processor for dividing eachcorresponding one of the second plurality of average temperature valuesby a corresponding one of the second plurality of temperature standarddeviations thereby determining a corresponding one of a second pluralityof homogeneity of skin temperature values for the plurality of subareas,and the processor for determining a second average homogeneity of skintemperature value from the second plurality of homogeneity of skintemperature values; and an analyzer for analyzing the first and secondpluralities of homogeneity of skin temperature values for each of theplurality of subareas, wherein when a spatial distribution of firstplurality of homogeneity of skin temperature values corresponding to acluster comprising at least six adjacent subareas is less than about 20%or more than about 100% of the first average homogeneity of skintemperature value, tissue corresponding to the cluster is preliminarilydetermined to be diseased, and wherein when tissue corresponding to thecluster is preliminarily determined to be diseased and when a spatialdistribution of second plurality of homogeneity of skin temperaturevalues corresponding to the cluster is less than about 20% or more thanabout 100% of the second average homogeneity of skin temperature value,tissue corresponding to the cluster is confirmed to be diseased.
 9. Anapparatus for detecting diseased tissue in accordance with claim 8,further comprising: a frequency analyzer for determining a firstcontributing frequency of the first plurality of homogeneity of skintemperature values for each of the plurality of subareas using the firstseries of thermal images, the frequency analyzer for determining firstlower and upper threshold frequencies using the first contributingfrequency of each of the subareas, the frequency analyzer fordetermining a second contributing frequency of the second plurality ofhomogeneity of skin temperature values for each of the plurality ofsubareas using the second series of thermal images, and the frequencyanalyzer for determining second lower and upper threshold frequenciesusing the second contributing frequency of each of the subareas, whereinthe analyzer is further adapted for analyzing the first and secondcontributing frequencies for each of the plurality of areas, whereinwhen a first spatial distribution of contributing frequencies of thecluster is less than the first lower threshold frequency or more thanthe first upper threshold frequency, tissue corresponding to the clusteris preliminarily determined to be diseased, and wherein when tissuecorresponding to the cluster is preliminarily determined to be diseasedand when a spatial distribution of second contributing frequencies ofthe cluster is less than the second lower threshold frequency or morethan the second upper threshold frequency, tissue corresponding to thecluster is confirmed to be diseased.
 10. An apparatus for detectingdiseased tissue in accordance with claim 8, further comprising: afrequency analyzer for determining a first contributing frequency of thefirst plurality of homogeneity of skin temperature values for each ofthe plurality of subareas using the first series of thermal images, thefrequency analyzer for determining a first average amplitude of thefirst contributing frequency for the plurality of subareas, thefrequency analyzer for determining a second contributing frequency ofthe second plurality of homogeneity of skin temperature values for eachof the plurality of subareas using the second series of thermal images,and the frequency analyzer for determining a second average amplitude ofthe second contributing frequency for the plurality of subareas, whereinwhen a spatial distribution of first amplitudes of first contributingfrequencies of the cluster is less than about 20% or more than about100% of the first average amplitude, tissue corresponding to the clusteris preliminarily determined to be diseased, and wherein when tissuecorresponding to the cluster is preliminarily determined to be diseasedand when a spatial distribution of second amplitudes of secondcontributing frequencies of the cluster is less than about 20% or morethan about 100% of the second average amplitude, tissue corresponding tothe cluster is confirmed to be diseased.
 11. An apparatus for detectingdiseased tissue in accordance with claim 8, wherein the analyzer furtheruses a fast Fourier transform analysis.
 12. An apparatus for detectingdiseased tissue in accordance with claim 8, further comprising: meansfor subjecting the predetermined area of tissue to a thermal stress whenthe imager records the first and second series of infrared images of thepredetermined area of tissue.
 13. An apparatus for detecting diseasedtissue in accordance with claim 12, wherein the means for subjectingcreates a flow of air.
 14. An apparatus for detecting diseased tissue inaccordance with claim 12, wherein the means for subjecting creates awater mist.